AI-Powered Cybersecurity Threat Detection System

A comprehensive cybersecurity solution that leverages artificial intelligence to detect and mitigate real-time security threats through network traffic and system log analysis.

Architecture Overview

Components

1. Frontend (React + TypeScript)

   • Modern Material-UI dashboard
   • Real-time threat visualization
   • Responsive design for all devices
   • JWT-based authentication

2. AI Service (Python + FastAPI)

   • TensorFlow-based threat detection
   • Real-time URL and domain analysis
   • SSL certificate validation
   • Security headers inspection

3. DevOps Infrastructure

   • Containerization with Docker
   • Kubernetes orchestration
   • GitHub Actions CI/CD pipeline
   • Infrastructure as Code with Terraform
   • Configuration management with Ansible

4. Monitoring Stack

   • Prometheus metrics collection
   • Grafana dashboards
   • Kubernetes health monitoring
   • Application performance metrics

Project Structure

.
├── Frontend/                 # React frontend application
│   ├── src/                 # Source code
│   ├── Dockerfile          # Frontend container configuration
│   └── package.json        # Dependencies and scripts
├── ai-service/              # Python AI service
│   ├── app.py             # FastAPI application
│   ├── web_analyzer.py    # Threat detection logic
│   ├── Dockerfile         # AI service container configuration
│   └── requirements.txt   # Python dependencies
├── k8s/                    # Kubernetes manifests
│   ├── frontend-deployment.yaml
│   └── ai-service-deployment.yaml
├── terraform/              # Infrastructure as Code
│   └── main.tf            # Terraform configuration
├── monitoring/             # Monitoring configuration
│   └── prometheus/        # Prometheus setup
├── ansible/               # Configuration management
│   └── site.yml          # Ansible playbook
└── .github/
    └── workflows/         # GitHub Actions CI/CD

Prerequisites

• Docker Desktop with Kubernetes enabled
• Terraform >= 1.0.0
• Ansible >= 2.9
• Node.js >= 18.x
• Python >= 3.9
• AWS CLI configured (for cloud deployment)

Quick Start

1. Local Development

   # Frontend
   cd Frontend
   npm install
   npm run dev
   
   # AI Service
   cd ai-service
   python -m venv venv
   source venv/bin/activate  # or .\venv\Scripts\activate on Windows
   pip install -r requirements.txt
   uvicorn app:app --reload

2. Docker Deployment

   # Build images
   docker build -t cybersecurity-frontend Frontend/
   docker build -t cybersecurity-ai ai-service/
   
   # Run containers
   docker-compose up -d

3. Kubernetes Deployment

   # Apply Kubernetes manifests
   kubectl apply -f k8s/
   
   # Check deployment status
   kubectl get pods -n cybersecurity

4. Infrastructure Deployment

   # Initialize and apply Terraform
   cd terraform
   terraform init
   terraform apply
   
   # Run Ansible playbook
   cd ../ansible
   ansible-playbook site.yml

Monitoring

• Prometheus: Access metrics at http://localhost:9090
• Grafana: Access dashboards at http://localhost:3000
  • Default credentials: admin/admin
  • Pre-configured dashboards for system and application metrics

CI/CD Pipeline

The GitHub Actions pipeline automatically:

1. Runs tests for both frontend and AI service
2. Builds and pushes Docker images
3. Deploys to Kubernetes cluster
4. Updates infrastructure using Terraform

Security Notes

• All sensitive data should be stored in Kubernetes secrets
• Update the .env files with proper credentials
• Configure proper CORS settings in production
• Enable network policies in Kubernetes
• Regular security audits recommended

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License - See LICENSE file for details